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Agile Team Perceptions of Productivity Factors


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In this paper, we investigate agile team perceptions of factors impacting their productivity. Within this overall goal, we also investigate which productivity concept was adopted by the agile teams studied. We here conducted two case studies in the industry and analyzed data from two projects that we followed for six months. From the perspective of agile team members, the three most perceived factors impacting on their productivity were appropriate team composition and allocation, external dependencies, and staff turnover. Teams also mentioned pair programming and collocation as agile practices that impact productivity. As a secondary finding, most team members did not share the same understanding of the concept of productivity. While some known factors still impact agile team productivity, new factors emerged from the interviews as potential productivity factors impacting agile teams.
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Agile Team Perceptions of Productivity Factors
Claudia Melo, Daniela S. Cruzes, Fabio Konand Reidar Conradi
Department of Computer Science, University of S˜
ao Paulo, Brazil
Department of Computer and Information Science, NTNU, Trondheim, Norway
({claudia,fabio.kon} ({dcruzes,conradi}
Abstract—In this paper, we investigate agile team perceptions
of factors impacting their productivity. Within this overall goal,
we also investigate which productivity concept was adopted by
the agile teams studied. We here conducted two case studies
in the industry and analyzed data from two projects that
we followed for six months. From the perspective of agile
team members, the three most perceived factors impacting
on their productivity were appropriate team composition and
allocation, external dependencies, and staff turnover. Teams
also mentioned pair programming and collocation as agile
practices that impact productivity. As a secondary finding,
most team members did not share the same understanding
of the concept of productivity. While some known factors still
impact agile team productivity, new factors emerged from the
interviews as potential productivity factors impacting agile
Keywords-productivity factors; agile methods; team produc-
tivity; empirical analysis
Low costs and time-to-market are the major drivers of
software productivity improvements [1], [2]. Several studies
have been carried out to address these issues by proposing
and analyzing processes, methods, tools, and best practices
[1], [3]–[6]. Despite efforts to increase software productivity,
delivering on time and at budget are notoriously poor; and
productivity has not shown much improvement over time
Although productivity has been studied intensely [2], it re-
mains a controversial issue. First, there are several concepts
involved in its definition, such as effectiveness, efficiency,
and performance, generating misunderstandings, and term
overload. Second, the software productivity measurement is
traditionally defined as the ratio of output (e.g., lines of
code, function points, or implemented features) divided by
input (e.g., time effort). In addition, software development
is mental work involving knowledge creation or, at least,
knowledge use as a dominant part of the work [8]. This
concept may change the way we observe and interpret
software productivity, since knowledge is complex and hard
to evaluate.
Software productivity is also influenced by many fac-
tors, such as project characteristics, product complexity, or
personnel skills [2], [9], [10]. Project managers are now
more aware of the importance of factors that influence
team productivity in projects [11]. To manage productivity
effectively, it is important to identify the most relevant
difficulties and develop strategies. Literature reviews [2], [9],
[10] describe factors having significant impact on software
productivity aiming to support companies with factor iden-
tification and selection.
Agile methods, such as Extreme Programming [12] and
Scrum [13], have evolved as approaches that simplify the
software development process. They aim to shorten the
development time as well as deal with the inevitable changes
resulting from market dynamics [14], [15]. Therefore, agile
methods have been increasingly adopted and rapidly joined
the mainstream of development approaches [16].
In a systematic review of empirical studies of agile
software development [17], Dyb˚
a and Dingsoyr investigate
what is currently known about the benefits and limitations of,
and the strength of evidence supporting agile methods. They
also identified four studies comparing the productivity of
agile teams with the productivity of teams using traditional
development methods; three of the four studies found that
using XP results in increased productivity. Other studies
have evaluated agile practices and productivity [18]–[21],
but as companies are increasingly adopting agile methods, it
is important to understand whether the factors as influencing
productivity remain the same.
In parallel with our research, Hannay and Benestad [22]
performed a study in productivity threats in a large agile de-
velopment project, in which they uncovered ten productivity
threats, as perceived by the project members. In particular,
Petersen [23] recommends that new productivity factors have
to be considered with the change in developing software,
and old factors need to be re-evaluated when there is a new
context. Our study aims to provide a better understanding of
factors influencing the productivity of agile teams, as well
as the concept of productivity they use. This research focus
on the following research questions and sub-questions:
RQ1. How do agile teams define productivity?
RQ2: What do agile team members identify as the main
factors impacting on productivity?
How do these factors impact positively, or nega-
tively on the productivity of agile teams?
RQ3: Which agile practices are perceived to impact on
a given team’s productivity?
For this purpose, we conducted two case studies in
the industry [24] to gain knowledge on the factors most
perceived as impacting productivity according to the agile
team members. Given the exploratory nature of our research,
we decided to interview teams directly and ask their opin-
ions, instead of using pre-existing productivity models (e.g.
COCOMO [4]).
The rest of this paper is organized as follows. Section
II gives an overview of software productivity factors found
in the literature. Section III explains the concept of knowl-
edge worker productivity. Section IV describes our research
method and design. Section V presents the results and
Section VI discusses the main findings and implications for
research and practice. Section VII describes some limitations
of this work. The last section concludes the paper and
describes future work.
We identified three literature reviews [2], [9], [10] on
productivity factors in software engineering. Table I presents
the main factors identified from these reviews based on the
most relevant sources and included only factors that had
some empirical work studying the effect of the factor on
productivity. We did not include studies that do not provide
novel findings on productivity factors, such as work that use
COCOMO factors without any new insight, or essays dis-
cussing productivity factors without any empirical evidence.
Moreover, as we are considering team productivity, we will
not discuss factors influencing individual or organizational
The factors in Table I include: product, personnel, project,
and process factors. Product is related to a specific charac-
terization of software, such as domain, requirements, archi-
tecture, code, documentation, interface, size, etc. Personnel
factors involve team member capabilities, experience, and
motivation. Project factors encompass management aspects,
resource constraints, schedule, team communication, staff
turnover, etc. Process factors include software methods,
tools, customer participation, software lifecycle, and reuse.
We highlighted factors that also appeared in our results.
A Knowledge Worker (KW) has been described as a
high-level employee who applies theoretical and analytical
knowledge, acquired via formal education and experience,
to develop new products or services [25]. Drucker states
that knowledge-worker productivity is strongly related to
the degree of autonomy, responsibility, and continuous life-
long learning a person experiences. He emphasizes that
productivity is not only a matter of quantity of output,
quality is at least as important [26].
ırez and Nembhard [8] summarized the most impor-
tant dimensions that define the knowledge worker produc-
tivity. Table II describes the dimensions we consider more
closely related to software development.
Table I
factors Description
Reuse of software products, processes,
and artifacts, including components,
frameworks, and software product lines.
System characteristics: architecture, com-
plexity, domain, non-functional require-
ments, stability requirements, user inter-
face, and software size.
and capabilities
Includes customer experience, domain
knowledge and experience, generational
experience, i.e., percentage of the devel-
opment team already participating in two
or more generations of software projects,
programming language experience, staff
capabilities and experience, and experi-
ence with tools.
Motivation Motivation to work on the project and in
the company.
Includes aspects of quality of manage-
ment, conflict management, task assign-
ment, and administrative and formal co-
Constraints such as timing, reliability,
storage, team size, and project duration.
Schedule Concerns schedule compression and ex-
Includes team size, team collocation, and
staff turnover.
Communication Includes informal and face-to-face com-
Refers to real customer involvement in
Daily builds Frequent integration of system compo-
Documentation Use of artifacts to register project and
product knowledge.
Early prototyping
Early Prototyping stage involves prototyp-
ing efforts to resolve potential high-risk
Incremental and
iterative develop-
Incremental approaches encompass vari-
ous ways of producing a sequence of parts
of a system, while iterative approaches
involve a diversity of ways of producing
parts of a system, trying them out, and
feedback on user experience of production
of new or revised parts.
Use of top-down requirements analysis
and design structured design notation,
structured code, etc.
Level of abstraction of the language (e.g.,
Java is a high level language)
methods Methodologies and practices
Tools usage Use of CASE tools, IDEs, etc.
The Agile Manifesto relies on certain principles, in which
the highest priority is to satisfy the customer through early
and continuous delivery of valuable software [20]. The
deliveries should be short-term and the delivered working
software measures the project progress. Productivity in agile
teams is therefore more related to the ability to manage
deliveries and meeting deadlines, while maintaining quality
and satisfying the customer. The main KW productivity
dimensions related to the agile principles are timeliness,
Table II
Dimensions Description (adapted from [8])
1. Quantity Accounts for outputs (quantities) and
outcomes (quantification of qualitative
variables such as customer and worker
2. Costs Accounts for profitability, costs, etc.
3. Timeliness Accounts for meeting datelines, overtime
needed to complete the work, and other time
related issues.
4. Autonomy Accounts for independence and how many
things a worker can do simultaneously.
5. Efficiency Accounts for doing things right. Refers to
any task, even if it is not important to the
job. The task is completed meeting all the
standards of time, quality, etc.
6. Quality Accounts for how good the work is.
7. Effectiveness Accounts for doing the right things. Refers
just to the tasks that are important to the
job, even if they are completed without
meeting standards of time, quality, etc.
8. Project
Accounts for overall result of work,
considering decision-making, team
interaction, communication, predictability,
crisis management, documentation,
transferability of work, etc.
9. Customer
Accounts for the fact that the product needs
to add value to the customer’s business.
quality, and customer satisfaction.
We aim to investigate which productivity concept the
teams use in their projects. We also want to identify which
factors impact on agile team productivity and, specifically,
how they impact on it. Finally, we want to explore whether
agile practices impact productivity from the teams’ view-
point. To answer our research questions we performed two
case studies in the industry [24], [27]. We chose to follow
the teams for 6 months because the influence of some
productivity factors may change across time, depending
on the project context. For instance, the problem of staff
turnover may be noticeable just after a member dismissal.
Therefore, if we did not ask the interviewee frequently about
specific factors that may have affected productivity, they may
have forgotten the effect of the dismissals.
The criteria for case selection were: companies using agile
(XP [12] and/or Scrum [13]) for at least 2 years; companies
in different business segments, geographical location, size,
structure, and culture; agile projects with, at least, four co-
located developers; in progress for at least 6 months; and in
different business areas. The unit of analysis is a set of two
development projects, one company each.
Moreover, we drew up a non-disclosure agreement for
companies to sign. This step was important to establish a
formal link between researchers and companies, and ensure
data confidentiality. The data collection was carried out in
two Brazilian companies from Sept. 2010 to Feb. 2011.
A. Research context
Company 1 is a large financial corporation with more
than 500 IT employees who had previously used plan-driven
development processes. The company managers decided to
adopt agile to increase team productivity. They have been
using agile for two years. Project 1 is a re-development
of an existing system for the financial market involving
several institutions. The project started in March 2010, and
is estimated to last for around two years. The team adopted
several XP [12] and Scrum [13] practices and used 1-week
Company 2 has been delivering e-commerce and infras-
tructure services for over ten years, and has used only
agile methods to develop software. It employs approximately
80 developers. Project 2 is a new development of an e-
commerce system in a market with other competitors. The
project also started in March 2010 but does not have a
specific deadline, since they are developing software as a ser-
vice, with continuous improvement and new functionalities.
The project adopts several XP, Scrum, and Lean principles
and practices.
Table IV describes the project profiles, considering guide-
lines provided by Kitchenham et al. [28]. Table III shows
the level of adoption of agile practices by each project. If
the team uses one practice fully, we assigned the term full.
If they use just a few recommendations of the practice, we
assigned the term partial. When they did not use it, we
assigned the phrase Do not use.
B. Data collection and analysis
The main data collection methods were semi-structured
interviews and informal face-to-face discussions with the
team members. The data was collected throughout a period
of 6 months, gathering opinions of different stages of the
The interviews were semi-structured (see the interview
guide in Appendix A) to understand the factors impacting
project productivity in the team’s perception and how they
impacted. One researcher conducted the interviews. Each in-
terview lasted approximately one hour, and the interviewees
were informed about the audio recording and its importance
to the study. We interviewed 13 team members (see Table
IV for more details), including developers, project managers
and product owners, considering also different experience
We used thematic analysis to analyze the data, a tech-
nique for identifying, analyzing, and reporting standards (or
themes) found in qualitative data [29]. According to Boyatzis
[29], themes that are relevant to describe a phenomenon.
To support the data analysis, we used a tool called NVivo
(version 9) that allows the information classification into
searchable codes.
After the interviews were transcribed, two researchers
performed the data coding, naming all the possible factors
Table III
Practices Project 1 Project 2
Code & Tests Full Full
Continuous integration Full Full
Daily deployment Do not Use Partial
Daily meeting Full Full
Energized work Partial Full
Incremental design Full Full
Pair programming Full Partial
Real customer involvement Full Full
Shared Code Full Full
Single code base Full Full
Sit together Partial Full
TDD Partial Full
Ten minute build Full Full
Negotiated scope contract Do not Use Partial
Planning game Full Partial
Retrospectives Full Full
Root cause analysis Do not Use Full
Slack Full Full
Stories Full Full
Team continuity Partial Partial
Weekly cycle Full Partial
Whole team Partial Partial
Table IV
Characteristics Project 1 Project 2
Team 6 full time developers,
2 part time develop-
ers, 1 scrum master,
1 project manager, 1
product owner (Total =
11 members)
4 full time develop-
ers, 2 part time devel-
opers, 1 project man-
ager/coach, 1 product
owner (Total = 8 mem-
Language Java Ruby
Reliability, Availabil-
ity, Performance
Reliability, Availabil-
Reuse High - Software prod-
uct lines
High - Open source
High stability Medium stability
Staff turnover Considered medium
by the project manager
Considered medium
by the project manager
Interviewees 7 (3 full time de-
velopers, 1 part time
developer, 1 product
owner, 1 scrum master,
1 project manager)
6 (1 project manager/
coach, 1 product
owner, 4 developers)
for productivity mentioned by the interviewees. Each code
was discussed before including in NVivo.
To answer research question RQ1, we used the taxonomy
of knowledge worker productivity proposed by Ramrez and
Nembhard [8]. For RQ2 and RQ3, we chose a data-driven
approach (Boyatzis [29] pp. 29-30), because we wanted the
themes to emerge from the data. This was important to
explore data without the influence of factors well known
in the literature.
We then classified the patterns according to themes and,
finally, all researchers interpreted and discussed the pat-
terns. The data from the interviews were analyzed for each
company and enabled the within-case analysis. Then, we
aggregated the results to compare both companies in a cross-
case analysis.
We describe below results from the interviews, focusing
on answering the research questions posed in the introduc-
A. RQ1. How do agile teams define productivity?
Based on the concepts of knowledge worker productivity
(Section III), we coded the answers in which the intervie-
wees were talking about being more or less productive. We
also coded any mention about their criteria to evaluate the
productivity variation.
In most of the interviews, the team member’s definition
for productivity was unclear for the researchers. Three
interviewees mentioned that timeliness is a criterion for
measuring or perceiving productivity. Three interviewees
also mentioned quantity, and two mentioned quality as a way
to determine productivity. It was surprising that only one
interviewee mentioned customer satisfaction as a criterion,
especially since if a team delivered high quality software
on time, without satisfying the customer, they would not
achieve the overall iteration/release goals.
B. RQ2: What do agile team members identify as the main
factors impacting on productivity?
During the data analysis, we identified a total of 89
codes concerning factors impacting on productivity of the
agile teams studied. The codes were organized into patterns
originating from the main themes. We also ranked the
themes by the number of sources, i.e., interviewees, who
mentioned them. Only the most cited themes (mentioned by,
at least, 30% of all interviewees) were selected to compose
the final list of factors. Table V presents the most mentioned
factors, a brief description, the percentage of citations in
each project, the impact of the factor on their productivity
(positive/negative), and the observed effects of the factor.
1) Team composition and allocation: Team composition
is the configuration of member attributes in a team [30].
According to Bell [31], the team composition could be
related to team performance because it affects the amount
of knowledge and skills that team members need to apply
to the team task.
As shown in Table V, appropriate team composition
and allocation is considered a factor impacting on team
productivity. The interviewees pointed out that small and
mixed teams, with the required expertise, and being full-time
allocated to the project are the most desirable attributes of
team composition.
Team composition with different profiles and levels of
knowledge was considered positive to team productivity,
especially in Project 1. One possible explanation is that
this team consists of business experts, experienced software
architects, and beginners. Experienced team members
contribute to the work adding knowledge, while the others
Table V
Factor Description % / Number of
responses Impact Effects
and allocation
Various specialists in the
team, even with different levels
of knowledge (mixed team)
Full time team members
Everyone required to be in-
volved with the system devel-
opment is a part of the team,
including the customer (whole
53% (7 out 13)
(4 from Project 1,
3 from Project 2)
(+) Mixed teams
o Experienced team members contribute to work
adding knowledge, while others contribute being
(+) Small teams
o Better communication and alignment among
team members
o Easier conflict management and coordination
o Sense of commitment and responsibility
(+) Full time team members
o Team more focused, without work interruptions
(-) Whole teams
o Important to have all required skills on the team,
which is difficult to provide
Need for external definitions
or explanations
Need for waiting for another
team to finish task, e.g. waiting
for customer acceptance or for
a component; interacting with
external customers; publishing
versions of system or of data
model across different environ-
ments (integration, homologa-
tion, production)
46% (6 out 13)
(3 from Project 1,
2 from Project 2) Negative
(-) Organizational definitions/policies do not consider
agile team needs
o Coordination of external dependencies is not
compatible with the agile project pace
(-) External teams do not consider themselves part of
o This compromises teamwork and focus on
project’s overall goal
Staff turnover
Exit or entry of people in the
project team
30% (4 out 13)
(2 from Project 1,
2 from Project 2)
(-) Loss of critical knowledge due to lack of docu-
mentation, even using job rotation
(+) Opportunity to learn new things and make im-
contribute flexibility, as stated by a developer:
Some things contribute to productivity. We have very
experienced people here, and less experienced ones that are
more flexible (Developer, Project 1).
A lack of necessary expertise was also mentioned as
a factor impacting on productivity. Both teams lacked
personnel to play specific (and necessary) roles in the
project, and consequently they faced problems completing
the tasks. The major concern was about the lack of high-
quality testing (for Project 1 and Project 2) and front-end
design capabilities (for Project 2). Despite being important
to have all the requisite skills on the team (the ’Whole
team’ agile practice), they are sometimes difficult to provide.
Very few people know certain things. Knowledge is very
focused on some things... I think we are missing a test
analyst to design test scenarios, because not everyone has
the knowledge to construct them (Developer, Project 1)
To increase productivity, I would like to have a front-end
staff on each teamThis profile is very hard to find (Project
Manager, Project 2)
The interviewees mentioned that small teams lead to
better communication and alignment. In addition, conflict
management and coordination among team members are
easier to deal with. Such responses confirms the findings
from other studies [32]–[34]. That is, as team size increases,
the number of necessary communication links between team
members increases and, there will be more potential conflicts
to manage.
In Project 2, some members left the project, and the
remaining team members were allocated full-time to the
project. A Developer said:
As we reduced the team, we are starting to focus more
on what we want. Before, it was a bit messy and people did
not perform certain tasks because they thought that other
people would do it (Developer, Project 2)
At the same project, the product owner also noticed the
benefits of the full-time allocation:
After the reorganization, nobody needs to move to other
activities outside the project. So, they are more focused.
Everyone knows everything in the project (Product Owner,
Project 2)
In Project 1, the team size increased over time and some
team members noted it as a negative factor impacting on
team productivity. From the team members’ perspective,
small teams also enable a better understanding of the big
picture of the product. This is because fewer people need to
learn and keep up to date on the product scope. In addition,
interviewees said that small teams help to increase the
team’s sense of responsibility and commitment.
2) External dependencies: There are several kinds of
dependencies in a project. Shared resources, prerequisite
constraints, simultaneity constraints, and the relationship of
tasks and subtasks are common examples of dependencies
among activities in a project [35]. External dependencies
are also mentioned as a factor impacting on the team
productivity (Table V).
Coordination processes are commonly used for managing
dependencies among activities [35], [36]. Companies
coordinate dependencies among teams or resources using
some coordination strategies. For instance, when Project 1
delivers the system to the test environment, it has to submit
some artifacts, such as data models, to the quality assurance
(QA) team. The QA team is an example of resource shared
among all enterprise projects of the company. It implements
a coordination process first come/first serve [35] to manage
requests from other teams. According to the team, this kind
of coordination solution is misaligned with the pace of the
agile project:
The other teams are not working at the pace of the project,
they are not working in the (same) way... The organization
is not ready yet (Developer, Project 1)
A similar problem occurs when Project 2 publishes the
system to the corporate environment:
Currently, there is one factor that we are dealing with
after a lot of feedback from our retrospective, which is about
the relationship among the boundaries of development, test-
ing, and production. Whenever we cross these boundaries,
a bottleneck occurs. (Project Manager, Project 2)
Another external dependency problem occurs when an
agile team decides to reuse components or existing systems,
building a system of systems. External components
and systems are often evolving and have their own
lifecycle. Sometimes, agile teams need to wait for some
components, which may compromise a timely delivery,
the unsynchronized agile release pattern [37]. Figure 1
illustrate the lack of synchronization among teams that are
working to the same project. The main team (Team A)
requests components or services from external teams, such
as Team B and C. While waiting for the requisitions to be
completed, the main team thinks it is on track. However,
when the planned system release date arrives, the team
slips in integration problems. Project 1 reuses several
components and has to implement interfaces to enable
communication with other systems. The problem with the
integration with components and systems is that:
The productivity at the end of the first phase of the project
will be somewhat lower because we will have solved a lot
of problems... which are the integration with other systems,
Figure 1. Unsynchronized agile release pattern (adapted from [37])
publishing to a server with other business components.
(Project Manager, Project 1)
Therefore, these solutions to manage dependencies are not
compatible with the needs of the team. For agile projects, it
is not only important to manage dependencies, but also to
resolve them in a timely manner. Thus, agile teams need to
be more synchronized to achieve this final goal [37].
The interviewees also mentioned that the external teams
sometimes are not committed to the project goal, but just
with the execution of the requested task. In general, they
do not consider themselves part of the team and tend to
emphasize their importance in the process. A Developer
There are a lot of roles, such as “I am the system admin-
istrator”, “I am the QA” They don’t understand that we’re a
multidisciplinary team. Reducing the conflict between these
roles can simplify our work process (Developer, Project 2)
3) Staff turnover: Staff turnover is a common team risk
for any software development project [38], [39]. Coram and
Bohner [40] state that high turnover in a project can lead to
loss of critical knowledge due to the lack of documentation.
Even when teams use code reviews and job rotation the loss
of a significant member of a team can still be catastrophic
[40]. In our study, interviewees mentioned it as a factor
impacting on team productivity (Table V).
There was some level of staff turnover in both teams
studied. The teams perceived lower productivity due to
staff turnover. In Project 1, at one specific time of the
project, many team members left the project at once. Project
2 experienced lower, but frequent turnover. Both projects
promote job rotation through pair programming.
There are things that impact the project negatively... One
is the staff turnover. People who started the project are no
longer here. Everybody now is new. (Developer, Project 1)
There was a drop in productivity at the end of the year
when two developers left the project. (Project manager,
Project 2)
Surprisingly, team members also mentioned a positive
influence of staff turnover: the opportunity for the team to
improve and grow. New team members can bring new ideas
and experiences, leading the team to a more mature level.
This not only relates to the team ability to deal with turbulent
environments, but also to the continuous learning ability.
We see new people’s arrival on the team in a different
perspective. Maybe their proposals seem to be awkward for
the team and they need to mature to a point at which maybe
the proposed ideas will be good.(Developer from Project
C. RQ3: Which agile practices are perceived to impact on
a given team’s productivity?
In order to answer RQ3, we coded all answers that relate
an agile practice to team productivity. We then ranked the
most cited practices and selected only those mentioned at
least by 30% of interviewees. Table VI summarizes the
two agile practices related to team productivity, a brief
description of the practices, the percentage of responses
used to rank them, the impact on team productivity, and
the perceived effects of the factor.
1) Pair programming: Pair programming is the XP
core practice and involves two programmers working
collaboratively to develop software. Dyb˚
a et al. [19]
conducted a meta-analysis on 18 studies regarding pair
and solo programming. As a result, the authors provide
guidelines for when to use pair programming based on
the programmer expertise and the task complexity. They
recommend that Intermediate and Senior developers should
not work in pairs to solve easy tasks, because it could
be counter-productive. Our results show that some team
members also consider using pair programming to solve
easy tasks a waste of time.
Despite being an advantage to use pair programming,
when some activities are very simple, we do them
individually. I mean, if you always work in pairs, I think
there are times when you will lose productivity. (Developer,
Project 1)
I think that pair programming brings a lot of benefit,
works very well, but should not be used 100% of the time.
Depending on the task, when it is very basic, repetitive, two
guys working on that is unnecessary. (Developer, Project 2)
On the other hand, pair programming is also seen to
contribute to increased satisfaction, keeping team members
motivated [18], [41]. The results from our interviews
were, however, different when interviewing intermediate
and senior developers. Developers mentioned feeling
demotivated when using pair programming all the time,
because, sometimes, it is tiring or the task is simply too
easy to be done in pairs. They also mentioned that, when
solving complex tasks, they feel demotivated to work in
pairs because they would like to have time to think about
the problem alone before discussing it.
I think that pair programming is productive, but I don’t
like to work in pairs because I’m introspective. Sometimes,
I like to be alone and start thinking, without much talking,
solving the problem there, developing my solution. (Devel-
oper, Project 1)
Sometimes pair programming doesn’t help. When you
don’t know in which direction you should go to solve
complex problems. You want to use your own usual process
to test things, trial and error, random exploration. This is
not easy to explain to the others. (Developer, Project 1)
2) Collocation: The collocation of teams is an approach
aiming to improve communication and collaboration among
team members. Both Scrum and XP recommend collocation
as an agile practice. When adopting this practice, companies
also hope for productivity enhancement [42], but there are
advantages and disadvantages with the use of collocation
in software development [42]–[45]. Some projects reported
significant productivity gains, and improvements in commu-
nication, spirit of teamwork, learning and motivation. Lack
of privacy, work interruptions, lack of individual recognition,
and some disconnection from the rest of the teams were
mentioned as the negative side of collocation.
Collocation was mentioned by 30% of our interviewees.
This result was stronger for Project 1. Team members in this
project explained that collocated work helps to overcome
the invisible existing barriers between teams in a hierarchi-
cal company. They noticed improvements in requirement
negotiations and risk mitigations through the socializing
atmosphere provided by the collocation.
The interviewees mentioned that their productivity vary in
function of the workspace layout. Both projects work radi-
cally collocated [42] but they mention that is not enough to
be in the same space. The layout (such as the desks positions
and proximity) may also impact on their productivity. This
factor was mentioned more by interviewees in Company
1 than in Company 2. This may be because Company 2
has invested in a customized layout that benefits working
in pairs, while Company 1 has kept the same traditional
workspace infrastructure.
Our results show that not only is productivity a diffuse
concept but also, especially in the agile teams studied, it
remains very much associated with quantity. In other words,
for many of the interviewees, team productivity is the ratio of
outputs by inputs. The story points delivered are the output
and the iteration is the unit for the input, i.e., it represents
the time spent to deliver the story points. Similarly, XP
Table VI
Agile practice Description % / Number of
responses Impact Effects
Pair programming is
the XP core practice
and involves two
programmers work-
ing collaboratively
to develop software
53% (7 out 13)
(4 from Project 1,
3 from Project 2)
(+) Enables knowledge dissemination
o Even when team members work on different schedules.
o Especially when people have different backgrounds (e.g.,
domain vs technical). .
(+) Helps in creating commitment, supportiveness and trust.
(+) Increases the level of communication.
(-) Full time pair programming
o Counter-productive and demotivating in easy tasks
o Controversial issue when resolving complex tasks - it de-
pends on the individual cognitive process of problem solving.
Consists of placing
team members near
each other.
30% (4 out 13)
(2 from Project 1,
2 from Project 2) Positive
(+) Helps in requirements negotiation and planning (re-planning)
(+) Improves communication and cooperation.
(+) Varies as a function of the workspace layout
o Even when the team is collocated, team members want to
be radically collocated
proposes project velocity as a productivity metric for team
performance [12]. The drawback of this metric is that many
teams use it as a way to show that the team is able
to move rapidly, without considering that they may not
necessarily be going in the right direction. The concepts
of timeliness,quality, and customer satisfaction were not
strongly associated with productivity by our interviewees,
which contradicts the priorities of an agile team posed by
the Agile Manifesto (see Section III).
Our results show that some traditional productivity factors
(highlighted in Table I) are still impacting software develop-
ment teams, even with the adoption of the agile practices. In
addition, some specific factors emerged from the interviews
as consequences of the use of agile practices in software
The factors impacting on productivity mentioned by our
agile teams suggest that adopting agile methods should
include several organizational actions in order to synchronize
interdependent teams. In addition, these actions must address
the integration of teams at an organizational level. Naturally,
the organizational actions may vary with the type and
severity of the team’s external dependencies. Furthermore,
collocation of teams seems not to be enough to reach high
productivity in agile teams, but it seems that there is a need
to invest in the workspace layout.
One striking result from our interviews was the connection
of pair programming with tasks and motivation. When
tasks are too easy or too complex, they may influence the
motivation to work in pairs. As we have not found references
to this phenomenon in the literature, we believe this is a
point for further investigation in the use of pair programming
for agile team productivity.
The following hypothesis can be derived from our results:
i) the modality of the coordination process used to manage
external dependencies impact on agile team productivity, due
to the need for synchronization between the teams involved;
ii) the degree of complexity of the task affects motivation
to work in pairs, which in turn, affects the productivity; iii)
productivity varies in function of the workspace layout, even
when teams are radically collocated. There is a need for
further investigation of these hypotheses in new empirical
In comparison with Hannay and Benestad’s study [46],
there are some differences in the context and results. The
context of their study consisted of 11 Scrum teams from
three different subcontractors working on the same project,
totaling up to 88 team members. Our two teams had 11 and 8
members in two different projects in two large companies. In
terms of results, Hannay and Benestad found ten problem
areas, and from these problems, only two were somehow
related to our results: excessive dependencies within the sys-
tem and difficulties when coordinating test and deployment
with external parties. These two problems can be seen as part
of the external dependency problem that was also mentioned
often by our interviewees.
Although we have discussed the implication of our results
for research and practice, the research reported here has
limitations. First, our study was limited to members of
two companies and two projects. The two projects had
different characteristics, helping to overcome some issues
with generalizability of our results. Within the projects,
we were careful to interview members playing different
roles on the team, so we could obtain different perspectives
of productivity. As we pointed out in the discussion, the
differences between our results and Hannay and Benestad’s
stress the need for further research to examine productivity
factors in a wider variety of organizational and team settings.
Another limitation of our study is that we relied upon
interviews to derive our results. The lack of standardization
that it implies inevitably raises concerns about reliability. To
reduce some of these concerns, we discussed and improved
the interview protocol iteratively before data collection.
One researcher also visited the companies many times to
better understand their context and observe the teams. The
interviews were also conducted in different moments of
the projects. For reliability of the analysis, two researchers
coded all interviews individually and in a second step, they
discussed each coding before including it in the software
tool. One reliability issue that we did not overcome was the
triangulation of the results with multiple methods of data
This study focused on investigating factors affecting pro-
ductivity of agile teams. Based on our results, it is reasonable
to conclude that there are some factors perceived more
as impacting on the productivity of agile teams, namely:
Team composition and allocation,External dependencies,
and Staff turnover. All such factors are related to project
management issues.
Regarding agile practices, pair programming and team
collocation were the most cited practices impacting team
productivity. As a secondary result, we found that the
concepts of timeliness,quality, and customer satisfaction
were not strongly associated with productivity by our inter-
viewees, which contradicts the highest priority of an agile
team in accordance with the Agile Manifesto [20].
A better understanding of the factors impacting on pro-
ductivity can enable software teams to design interventions
that allow software development teams to deliver software on
time, with the desired quality and satisfying their customers.
The results of this study also indicate a number of direc-
tions for future research. We plan to complement the analysis
performed in this paper with data from direct observation,
team retrospectives, and burndown charts and by adding new
companies and project data. Furthermore, we aim to design
studies addressing the hypotheses that emerged during this
We are sincerely grateful to Dr. Tore Dyb˚
a for his valuable
contribution to this work. We would like to thank the
companies and their employees who contributed to this
project. This research is supported by FAPESP, Brazil, proc.
2009/10338-3, CNPq, Brazil, proc. 76661/2010-2, and the
Research Council of Norway, proc. 202657.
Interview guide
What is your role in the project and how long have you been
working on it?
How is the team’s way of work? What is your role in the
How does the functionality prioritization work?
How is the judgment done regarding the value that the team
wants to deliver during prioritization?
In you opinion, has the customer realized the delivered value
in each iteration and release? Does he report this?
In your opinion, has the team delivered value to the customer?
What is the project status (scope, cost, time box, etc.)?
What’s your opinion regarding the project productivity? How
do you realize this productivity?
What’s you opinion about project quality? How do you track
the external quality? And internal? How do you handle the
Is there any re-work level on the project?
What do you think that most influences your team productiv-
In your opinion, which changes were recently done in the
team way of work that can have influenced any productivity
Do you consider the project motivating?
Is there anything demotivating you in the project? And at the
Is there anything in the agile methods that motivates you in
Is there any kind of wasting which jeopardizes the project?
If you could choose three things to increase the productivity,
what would it be?
Do you think that the agile methods usage increases the team
productivity? Why? Is there something in agile that helps your
own productivity or the productivity of your team?
Is there anything in the agile methods that decreases your
individual productivity or the team productivity? If so, what
is it? Why?
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... While in traditional software development methodologies, productivity is measured by the number of Lines of Code (LOC) or Function Points (FP) per time (hour /month) by developers or teams, in agile software development, it is measured in another way. In Agile methods, productivity is mainly addressed by delivered Story Points in each iteration [2,3]. Agile methods are known for achieving high team productivity, due to practices like planning, executing in short cycles called iterations, and having self-organizing cross-functional teams [4]. ...
... The factors are comprised of team combination and allocation, external dependencies, and turnover of people in the organization. Based on agile technical methods, pair programming and team gathering have the most impact on team productivity [2]. Melo showed that team size, variety, personality, proficiency, collocation, and devotion of time are among the key factors to be considered when designing agile teams. ...
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Get access Cite article Share options Information, rights and permissions Metrics and citations Abstract Drawing on leadership theory, this research verified what makes shared leadership an effective form of leadership for agile project teams, and whether using it influences the outcomes achieved by such teams as well as the more distal outcomes. Survey data were collected from 251 members of agile project teams implementing projects of an iterative and incremental character. Structural equation modeling (PLS-SEM) was adopted to test the hypotheses. Our research confirms that shared leadership is an effective form of leadership for agile project teams whose members are empowered to engage in leadership functions or processes. The findings confirm a positive direct impact of shared leadership on the performance of agile project teams and indirect impact on project efficiency and effectiveness. The research results also confirm the influence of project team–related contextual moderators on shared leadership inputs and outputs. The study contributes to leadership theory in the plural leadership research stream and confirms the shift from individual leadership to collective leadership as a result of the growing popularity of the agility paradigm.
The importance of the team, its internal dynamics, and its performance are widely recognized within the software engineering community. While popular frameworks identify wholeness, stability over time, and smallness as important factors, they offer little guidance on how to form teams that achieve these three characteristics. The objective of this study is to investigate how these team characteristics interact in large‐scale software development contexts, particularly focusing on the impact of stable and dynamic teaming approaches. This was done through a multivocal study of literature, followed by individual semi‐structured interviews with 19 engineers from two companies and validation workshops with an additional two companies from unrelated industry segments. The study results show that the question of stable versus dynamic approaches to forming software engineering teams is largely unaddressed in industry, with stable teams representing a habitual default option. Meanwhile, both stable and dynamic teams clearly have respective strengths and weaknesses, calling for careful consideration of the most suitable approach in any given situation. To support such consideration, this paper presents a model of how team stability, wholeness, and smallness interact. This model is found relevant, accurate, generalizable, and useful by practitioners.
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La productividad de equipo en el desarrollo ágil de software (ASD, por sus siglas en inglés) está compuesta por un conjunto de factores que permiten evaluar el desempeño de cada uno de los integrantes de un equipo, aspecto crucial para establecer el éxito o fracaso de un proyecto. El propósito de esta investigación fue determinar la percepción que tienen los profesionales de equipos ASD sobre la medición de productividad partiendo de los factores identificados en un estudio preliminar. Para tal fin, se aplicó una encuesta que se orientó bajo el protocolo de Kitchenham y Pfleeger a ochenta y dos integrantes de equipos ASD. Los principales resultados señalaron que los profesionales de la industria de software asocian la productividad como un indicador de mejora dentro de los procesos del equipo y en el cumplimiento de objetivos a un cliente. Dentro de los factores que los participantes consideraron fundamentales en la medición de productividad de equipo en ASD se destacan la velocidad, la capacidad de trabajo y la satisfacción del cliente. Finalmente, los factores que afectan el desempeño de un equipo cuando se adaptan al cambio y los relacionados a la identidad del equipo, no fueron mencionados por los profesionales como parte del proceso de evaluación actual sobre productividad en equipos ASD, pero si fueron considerados como relevantes para ser posteriormente incluidos.
Purpose The purpose of this paper is to perform a comparative analysis between the productivity metrics recommended in the literature and those that companies in the knowledge-intensive services sector use in practice. Design/methodology/approach To collect information, a systematic review of the literature was used, to apply virtual surveys and interviews among managers of different companies representing the sector. For data analysis, categorical optimal scales, homogeneity tests, tetrachoric correlation matrices, word clouds and association coefficients for dichotomous variables were used. Findings There are association patterns between the metrics used and the nature of the work performed. Despite the heterogeneity observed in the productivity metrics, categorization guidelines related to the traditional, human resources and customer-oriented approaches emerge. Practical implications Possible neglects using metrics aimed at valuing the intellectual capital immersed in human resources are evident, particularly in the follow-up to autonomy, knowledge management, human capital, teamwork, training and capacity building metrics, among others. Conversely, face-to-face monitoring metrics, such as absenteeism, are overvaluation. Originality/value The approaches and metrics discussed and the results obtained, provide information so that knowledge-intensive companies have a reference framework to identify and select useful metrics to assess the work carried out by their workforce.
The book, The Mythical Man-Month, Addison-Wesley, 1975 (excerpted in Datamation, December 1974), gathers some of the published data about software engineering and mixes it with the assertion of a lot of personal opinions. In this presentation, the author will list some of the assertions and invite dispute or support from the audience. This is intended as a public discussion of the published book, not a regular paper.